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Enregistrement W6946525850 · doi:10.34944/dspace/7172

DOES MAJOR MATTER? AN EXAMINATION OF UNDERGRADUATE MAJOR AND MEDICAL SCHOOL ADMISSION

2021· other· en· W6946525850 sur OpenAlexaboutno aff

Notice bibliographique

RevueTUScholarShare (Temple University) · 2021
Typeother
Langueen
DomaineChemistry
ThématiqueWood and Agarwood Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésEntrance examCLARITYMedical schoolConstruct (python library)Flexibility (engineering)InstitutionCurriculumVariety (cybernetics)

Résumé

récupéré en direct d'OpenAlex

The official stance of the Association America of Medical Colleges (AAMC) regarding the undergraduate major of applicants for admission to medical school is that there are no required or preferred majors. While the AAMC is the body that governs admission to allopathic medical schools in the United States, this statement does not provide clarity to prospective medical school applicants as to what undergraduate major to select; it only encourages students from a variety of educational backgrounds to apply. Furthermore, a broad statement about undergraduate major flexibility does not indicate how choice of major will eventually impact admission to medical school. While the AAMC encourages applicants to choose any undergraduate major they wish, there is minimal peer-reviewed research or empirical evidence of the relationship between applicants’ undergraduate major and their likelihood of admission to medical school. Through the lens of the student-choice construct, this dissertation sought to determine if applicants’ undergraduate major is a statistically significant predictor of successful admission to medical school. This model accommodates decisions such as the intent to pursue post-secondary education, which institution to attend, what major to choose, and whether to persist to degree completion. The student-choice construct also contends that these decisions are influenced by the amount of human, financial, social, and cultural capital available to the student throughout the decision-making process. To study how choice of major impacts admission to medical school, I conducted a quantitative study using a hierarchical binary logistic regression. Secondary data were collected using the formal data request procedure outlined by the AAMC. Application-level data were received from the AAMC, and personally identifiable information including applicants’ names, identification numbers, and addresses were removed by the AAMC before the data were delivered. Additionally, given that the study involves the analysis of de-identified extant data, this study received exemption from the Institutional Review Board at Temple University. The dataset included 53,371 applicants to allopathic medical school for the 2019 application cycle. These applicants attended undergraduate institutions primarily located in the United States and Canada. The study revealed that undergraduate major does not serve as a statistically significant predictor of admission to medical school over and above applicants’ demographic characteristics, MCAT scores, and undergraduate grade point average. Applicants who chose a Biology, Chemistry, Physics, or Mathematics (BCPM) major did not have a greater chance of being admitted to medical school than an applicant who chose a non-BCPM major. These findings are consistent with previous studies that sought to predict variables that contribute to medical school admission. Future research should investigate the predictive ability of admissions variables such as applicant characteristics captured from medical school interviews; letters of recommendation; personal statements and community service, leadership, and healthcare experiences. A combined or comparative study similarly analyzing applicants to different health profession programs might also be useful. In addition, a non-binary categorization of specific undergraduate majors would provide an even more nuanced analysis of how different majors predict admission to medical school.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,446
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,1310,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,017
Tête enseignante GPT0,259
Écart entre enseignants0,242 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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